Practical voting rules with partial information

Voting is an essential mechanism that allows multiple agents to reach a joint decision. The joint decision, representing a function over the preferences of all agents, is the winner among all possible (candidate) decisions. To compute the winning candidate, previous work has typically assumed that voters send their complete set of preferences for computation, and in fact this has been shown to be required in the worst case. However, in practice, it may be infeasible for all agents to send a complete set of preferences due to communication limitations and willingness to keep as much information private as possible. The goal of this paper is to empirically evaluate algorithms to reduce communication on various sets of experiments. Accordingly, we propose an iterative algorithm that allows the agents to send only part of their preferences, incrementally. Experiments with simulated and real-world data show that this algorithm results in an average of 35% savings in communications, while guaranteeing that the actual winning candidate is revealed. A second algorithm applies a greedy heuristic to save up to 90% of communications. While this heuristic algorithm cannot guarantee that a true winning candidate is found, we show that in practice, close approximations are obtained.

[1]  Toby Walsh.  Complexity of Terminating Preference Elicitation , 2008, AAMAS.

[2]  Andrew N. Radford,et al.  The higher the better: sentinel height influences foraging success in a social bird , 2009, Proceedings of the Royal Society B: Biological Sciences.

[3]  Boi Faltings,et al.  Open constraint programming , 2005, Artif. Intell..

[4]  Vincent Conitzer,et al.  Vote elicitation: complexity and strategy-proofness , 2002, AAAI/IAAI.

[5]  Tuomas Sandholm,et al.  Generalizing preference elicitation in combinatorial auctions , 2003, AAMAS '03.

[6]  Toby Walsh,et al.  Winner Determination in Sequential Majority Voting , 2007, IJCAI.

[7]  Toby Walsh,et al.  Uncertainty in Preference Elicitation and Aggregation , 2007, AAAI.

[8]  Alexander Artikis,et al.  Voting in Multi-Agent Systems , 2006, Comput. J..

[9]  Toby Walsh,et al.  Incompleteness and Incomparability in Preference Aggregation , 2007, IJCAI.

[10]  Francesca Rossi,et al.  Dealing with Incomplete Preferences in Soft Constraint Problems , 2007, CP.

[11]  Toby Walsh,et al.  Aggregating Partially Ordered Preferences , 2009, J. Log. Comput..

[12]  Milind Tambe.  Electric Elves: What Went Wrong and Why , 2006, AI Mag..

[13]  Michael Wooldridge,et al.  Proceedings of the second international joint conference on autonomous agents and multiagent systems (AAMAS) , 2003 .

[14]  Vincent Conitzer,et al.  Communication complexity of common voting rules , 2005, EC '05.

[15]  Justin Zhijun Zhan,et al.  Privacy-Preserving Collaborative E-Voting , 2008, ISI Workshops.

[16]  Vincent Conitzer,et al.  Determining Possible and Necessary Winners under Common Voting Rules Given Partial Orders , 2008, AAAI.

[17]  Kathrin Konczak.  Voting Theory in Answer Set Programming , 2006, WLP.

[18]  Jérôme Lang,et al.  Voting procedures with incomplete preferences , 2005 .